Backfilling missing microbial concentrations in a riverine database using artificial neural networks

• Published in: Water research (Oxford) Vol. 41; no. 1; pp. 217 - 227
• Main Authors: Chandramouli, V., Brion, Gail, Neelakantan, T.R., Lingireddy, Srinivasa
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 2007
• Subjects: animal pathogens; Artificial neural networks; Atypical bacteria; Backfilling; Bacteria - growth & development; coliform bacteria; data analysis; data backfilling; data collection; decision making; Fecal coliform bacteria; Feces - chemistry; Feces - microbiology; Forecasting; Freshwater; microbial contamination; Models, Statistical; neural networks; Neural Networks (Computer); population density; prediction; regression analysis; relative strength effect; rivers; surface water; Water - chemistry; water treatment; watersheds; ANN; MRE; AC; RSE; BG; Artificial neural networks; Atypical bacteria; Fecal coliform bacteria; TC; Backfilling; CFU; FC; MSE
• ISSN: 0043-1354; 1879-2448
• DOI: 10.1016/j.watres.2006.08.022

Predicting peak pathogen loadings can provide a basis for watershed and water treatment plant management decisions that can minimize microbial risk to the public from contact or ingestion. Artificial neural network models (ANN) have been successfully applied to the complex problem of predicting peak pathogen loadings in surface waters. However, these data-driven models require substantial, multiparameter databases upon which to train, and missing input values for pathogen indicators must often be estimated. In this study, ANN models were evaluated for backfilling values for individual observations of indicator bacterial concentrations in a river from 44 other related physical, chemical, and bacteriological data contained in a multi-year database. The ANN modeling approach provided slightly superior predictions of actual microbial concentrations when compared to conventional imputation and multiple linear regression models. The ANN model provided excellent classification of 300 randomly selected, individual data observations into two defined ranges for fecal coliform concentrations with 97% overall accuracy. The application of the relative strength effect (RSE) concept for selection of input variables for ANN modeling and an approach for identifying anomalous data observations utilizing cross validation with ANN model are also presented.